The paper introduces a fully distributed continuous‑time algorithm for solving Generalized Nash Equilibrium Problems (GNEPs) with shared linear equality constraints. Unlike existing methods that require exchanging Lagrange multipliers, this approach converges to any GNE without multiplier communication, thereby reducing communication overhead and enhancing privacy. Discrete‑time variants are also presented and the method is demonstrated on a multi‑robot placement task.
By Shao-An Yin, Mingyi Hong, Nicola Elia
arXiv:2409. 19279v2 Announce Type: replace-cross Abstract: Continuous-time models can reveal accelerated structures in distributed optimization, but their rates need not survive direct discretization.
By Kushal Chakrabarti, Mayank Baranwal
arXiv:2606. 28307v1 Announce Type: cross Abstract: We analyze Bregman ADMM for nonconvex linearly constrained problems under two-sided relative smoothness, a condition that replaces the standard Lipschitz gradient assumption with a Hessian comparison relative to a Bregman kernel.
By Shuang Li, Zhihui Zhu, Qiuwei Li
arXiv:2609.14953v1 Announce Type: cross
Abstract: This paper aims to develop new and efficient distributed algorithms for solving a class of monotone inclusions, $0 \in \sum_{i=1}^n (G_ix + T_ix)$, o...
By Nghia Nguyen-Trung, Ion Necoara, Quoc Tran-Dinh
arXiv:2609.13925v1 Announce Type: cross
Abstract: This work studies the stability and convergence of augmented primal-dual dynamics when constraint values are estimated from samples. Unbiased constra...
By Kang Liu, Mengxiao Chen, Siqi Xiong, Yi Xia
arXiv:2606. 00759v1 Announce Type: new Abstract: Recent advances in artificial intelligence have expanded the focus from classical optimization to include equilibrium analysis in noncooperative games.
By Shao-An Yin
arXiv:2504. 12742v2 Announce Type: replace Abstract: Decentralized Federated Learning (DFL) enables collaborative model training without relying on a central server.
By Yuan Zhou, Xinli Shi, Xuelong Li, Jiachen Zhong, Guanghui Wen, Jinde Cao
The paper introduces Dec-BFTRL, a decentralized algorithm for online optimization of upper-linearizable payoffs with efficient separation access, targeting continuous diminishing-return submodular maximization. Each agent evaluates its action against the average of local objectives, projects via an approximate gauge, exchanges a cumulative surrogate-gradient dual state, and uses a local HybridNewton step to minimize its BFTRL potential. The method achieves an expected network-aggregate regret of “~O(√T)” while requiring T neighbor-mixing steps and ~O(T) separation-oracle calls per agent, and provides four wrapper instantiations for three DR-submodular problems.
By Yiyang Lu, Mohammad Pedramfar, Vaneet Aggarwal
We study decentralized online optimization of upper-linearizable payoffs over an action set under efficient separation access, with applications to online continuous diminishing-return (DR) submodular...
SPADE-DFL is a communication‑efficient decentralized federated learning algorithm that uses a primal–dual method to allow the number of local function‑value updates between neighbor exchanges to increase with the computation budget while maintaining non‑private convergence rates. For smooth nonconvex objectives, it achieves a time‑averaged stationarity and consensus bound of ≠O(T−1/3) with only ≠Theta(T−2/3) communication rounds, where T is the number of local updates per client. The method also supports client‑level differential privacy by isolating data‑dependent increments, proving privacy for the full interactive transcript and quantifying the resulting optimization error, and demonstrates higher mean test accuracy than existing decentralized learning methods on four classification tasks.
By Mengli Wei, Mengkai Zhu, Jiawen Chen, Wenwu Yu, Duxin Che
arXiv:2607. 01755v1 Announce Type: cross Abstract: In this paper, we consider the nonsmooth nonconvex decentralized optimization problem, where inter-agent communication is compressed.
By Siyuan Zhang, Nachuan Xiao, Xin Liu
NashDreamer is a new model-based reinforcement learning framework designed for two-player zero-sum imperfect-information games. It introduces a centralized Multi-Agent Recurrent State-Space Model that separates environment dynamics from player strategy effects, enabling the use of any policy gradient algorithm while preserving convergence guarantees to Nash equilibria. Experiments on four benchmark games show that NashDreamer achieves significantly better sample efficiency than model-free baselines early in training, and the authors analyze its optimization landscape, noting a potential vulnerability to posterior collapse in stochastic settings.
By Tom\'a\v{s} Hole\v{c}ek, Viliam Lis\'y